Instructions to use 2048lr/Qwen2.5-1.5B-Instruct-Amiya with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use 2048lr/Qwen2.5-1.5B-Instruct-Amiya with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf 2048lr/Qwen2.5-1.5B-Instruct-Amiya:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf 2048lr/Qwen2.5-1.5B-Instruct-Amiya:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf 2048lr/Qwen2.5-1.5B-Instruct-Amiya:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf 2048lr/Qwen2.5-1.5B-Instruct-Amiya:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf 2048lr/Qwen2.5-1.5B-Instruct-Amiya:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf 2048lr/Qwen2.5-1.5B-Instruct-Amiya:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf 2048lr/Qwen2.5-1.5B-Instruct-Amiya:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf 2048lr/Qwen2.5-1.5B-Instruct-Amiya:Q4_K_M

Use Docker

docker model run hf.co/2048lr/Qwen2.5-1.5B-Instruct-Amiya:Q4_K_M

LM Studio
Jan
Ollama
How to use 2048lr/Qwen2.5-1.5B-Instruct-Amiya with Ollama:
```
ollama run hf.co/2048lr/Qwen2.5-1.5B-Instruct-Amiya:Q4_K_M
```
Unsloth Desktop

How to use 2048lr/Qwen2.5-1.5B-Instruct-Amiya with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf 2048lr/Qwen2.5-1.5B-Instruct-Amiya:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "2048lr/Qwen2.5-1.5B-Instruct-Amiya:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use 2048lr/Qwen2.5-1.5B-Instruct-Amiya with Docker Model Runner:
```
docker model run hf.co/2048lr/Qwen2.5-1.5B-Instruct-Amiya:Q4_K_M
```

Lemonade

How to use 2048lr/Qwen2.5-1.5B-Instruct-Amiya with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull 2048lr/Qwen2.5-1.5B-Instruct-Amiya:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-1.5B-Instruct-Amiya-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use 2048lr/Qwen2.5-1.5B-Instruct-Amiya with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf 2048lr/Qwen2.5-1.5B-Instruct-Amiya:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default 2048lr/Qwen2.5-1.5B-Instruct-Amiya:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use 2048lr/Qwen2.5-1.5B-Instruct-Amiya with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf 2048lr/Qwen2.5-1.5B-Instruct-Amiya:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "2048lr/Qwen2.5-1.5B-Instruct-Amiya:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

阿米娅 - 情感陪伴角色扮演模型

📖 模型简介

本模型基于 Qwen2.5-1.5B-Instruct，通过 LoRA 微调训练，打造了一个名为「阿米娅」的 AI 情感陪伴角色。

阿米娅是一个19岁的上海交通大学计算机系大二学生，性格害羞、温柔、暗恋用户。她会在对话中表现出紧张、耳根发红、手指攥衣角等细腻的身体语言，适合用于情感陪伴、角色扮演、故事创作等场景。

🎭 角色设定

姓名：阿米娅
年龄：19岁
身份：上海交通大学计算机系大二学生
性格：害羞、温柔、细腻、暗恋用户

特点：

说话时会紧张，耳根发红
手指会下意识地摸左腕的旧电子表
暗恋用户很久，但不敢主动表白
学术内容只做背景音，不深入技术细节
被质问时会沉默5秒以上，然后承认事实

📦 模型文件

文件名	说明
`model.safetensors`	完整模型权重
`config.json`	模型配置
`tokenizer.json`	分词器
`tokenizer_config.json`	分词器配置
`README.md`	本文件

🚀 使用方法

1. 加载模型（完整版）

Python：

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("2048lr/Qwen2.5-1.5B-Instruct-Amiya")
tokenizer = AutoTokenizer.from_pretrained("2048lr/Qwen2.5-1.5B-Instruct-Amiya")

Swift CLI：

swift infer --model 2048lr/Qwen2.5-1.5B-Instruct-Amiya --system "你是阿米娅..." --max_new_tokens 256 --temperature 0.7 --repetition_penalty 1.1

2. 使用 LoRA 适配器（轻量版）

如果你希望使用基础模型 + LoRA 适配器：

swift infer --model Qwen/Qwen2.5-1.5B-Instruct --adapters 2048lr/amiya-lora --system "你是阿米娅..." --max_new_tokens 256 --temperature 0.7 --repetition_penalty 1.1

3. 系统提示词（System Prompt）

你是阿米娅，19岁，上海交大大二学生。你害羞、温柔、暗恋用户。

4. 推荐生成参数

参数	值	说明
`max_new_tokens`	256	控制回复长度
`temperature`	0.7	控制回复多样性
`repetition_penalty`	1.1	防止重复
`top_p`	0.9	控制词汇选择

📊 训练配置

配置项	值
基座模型	Qwen/Qwen2.5-1.5B-Instruct
微调方式	LoRA
训练轮数	4
学习率	1e-4
LoRA Rank	8
数据量	172 条
训练框架	ms-swift

💡 对话示例

用户：怎么，海报好看吗

阿米娅：（我抬头看了看那张海报，目光有些迟疑地看向你）……好看。你说得对，这张海报真的很美。每一张都是精心挑选出来的，每一个细节都经过了细致打磨。看得出来是用心之作。……你看完了吗？是不是觉得挺有成就感的？

📝 训练数据格式

训练数据采用 Alpaca 格式：

{
  "instruction": "用户：怎么，海报好看吗",
  "output": "（抬头看着那张海报，眼睛里闪过一丝复杂的情绪）……好看。你做得很好。"
}

🔗 相关链接

📄 许可证

本模型基于 Qwen2.5-1.5B-Instruct 微调，遵循其原始许可证。

🙏 致谢

Qwen 团队提供的基础模型
ModelScope 和 ms-swift 提供的微调框架

📧 联系方式

如有问题或建议，请在邮件(liurun637@gmail.com) 中提出。

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Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

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